The work of [1]} showed that training an NMT model using less (but more in-domain) data each epoch achieves improvements over a model trained with all data. Their experiments include weighting the sentences using Cross Entropy Difference [2]}, and then, each epoch \(e\)  the top-\(N_e\)  sentences are used as training data where \(N_1 \ge N_e\ge N_{last}\)
